Practical Synthetic Data Generation by Khaled El Emam

Author:Khaled El Emam
Language: eng
Format: epub
Publisher: O'Reilly Media
Published: 2020-05-18T16:00:00+00:00

Figure 4-22. A dashboard summarizing the utility metrics for a synthetic dataset

In terms of limitations of the framework, we examined all variables and all models in our utility framework, then summarized across these. In practice, some of these variables or models may be more important than others, and will be driven by the question being addressed in the analysis. However, this framework still provides more meaningful results than generic data utility metrics, which would not reflect all workloads.

Note that in this chapter we focused on cross-sectional data. For longitudinal data, other types of utility metrics may be needed. This is a more complex topic because it is more dependent on the type of data (e.g., health data versus financial data).

In the next chapter, we examine in more detail how to generate synthetic data. Now that we know how to assess data utility, we can more easily compare alternative synthesis methods.

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